1. Field of the Invention
The present invention relates to a pattern recognition method and a pattern recognition apparatus for recognizing a specific pattern such as a face and a person.
2. Description of the Related Art
There is a pattern recognition technique for detecting and identifying a predetermined pattern from an input pattern. Pattern recognition technique has progressed alongside performance increases in computers, and recently a system of detecting a face in an image in real time has started to come out. There is also a known face recognition technique that detects a face area in an image and verifying whom the person in the face area is. The accuracy of the face recognition technique is significantly affected by illumination conditions, such as oblique light, as well as face orientation variations, expression variations, and occlusions. The variations are not independent from each other. Therefore, the realization of a robust face recognition algorithm for the variations is difficult.
Consequently, techniques for handling the variations are proposed. The configuration of Japanese Patent Laid-Open No. 2000-90191 (hereinafter, “Document 1”) includes a plurality of inverse transformers that remove variation factors in an input image to perform variation-robust face recognition. The inverse transformers execute processes of removing face orientations, inclinations, and face misalignments (inverse transform of transformation). There are identifiers at the latter parts of the inverse transformers, and the identifiers execute matching processes of the output of the inverse transformers and dictionary data and output identification results. Ultimately, the result with maximum output is extracted from the plurality of identification results, and the result is set as the final identification result. Furthermore, an example of the processes of removing the variations includes a perturbation method.
Japanese Patent No. 4161659 (hereinafter, Document 2) proposes a method of obtaining similarities between a plurality of corresponding local areas of an input image and a registered image and performing recognition based on an integrated similarity obtained from several upper similarities. The upper similarities are obtained by a threshold process of a threshold dynamically determined by other plurality of similarities. Therefore, the threshold process removes the similarities between unclear areas due to variation factors. Ultimately, recognition resistant to the variations is attained by outputting an identification result using the integrated similarity.
However, the inverse transform process of Document 1 may delete feature quantities indicating individual differences, and the recognition accuracy may be affected. The execution of an inverse transformation process for removing all predicted variation factors from the input image increases the processing cost. The similarity of the local area in Document 2 tends to be high if variations, such as expressions and illumination variations, are similar, even if the persons in the input image and the dictionary image are different. Therefore, the integrated similarity is obtained by integrating the results of local areas with similar variations, and the recognition accuracy may be reduced.